Disease incidence, the rate of occurrence of new cases in a population, is typically much more difficult to estimate than prevalence, the fraction of the population having a condition at a given point in time. For transient conditions, such as seasonal flu, prevalence is a good proxy for recent incidence. However, for enduring conditions, such as HIV, current prevalence depends in detail on historical incidence, demography, and survival.

HIV epidemiology is one of the most urgent contexts in which a difficult-to-measure incidence plays a crucial role. Reliable estimates of HIV incidence are critical for epidemiological monitoring, understanding transmission patterns, and in the design and evaluation of intervention or prevention programs.

Traditionally, epidemiologists have referred to the counting of infection events during the prospective follow-up of an initially uninfected cohort as producing ‘directly observed’ incidence estimates. For population-level surveillance, this approach is often impractical and prone to bias. Indeed, the very definition of incidence, or any other population dynamic rate, is subtle and potentially problematic, especially when dealing with populations and conditions which are highly heterogeneous.